Probabl Skills

A set of skills to steer your AI-assisted machine learning experiments. The skills help you:

• build your machine learning pipeline with core data science libraries

• evaluate and store your results so you can easily audit and get insights from them
• connect your agent to Skore Hub to get a comprehensive view of

• iterate on your next experiments using insights from Skore diagnostics and your own

• organize your workspace according to best practices for data science projects

Probabl skills let you focus on the science while AI agents handle the implementation, guided by two important ingredients: core data science libraries for maintainability and methodological best practices for running your machine learning experiments properly.

In practice, from a prompt such as:

╭────────────────────────────────────────────────────────────────────────╮
│ > Given the context in the file data/README.md and the data located  │
│   in data/, let's build a first machine learning pipeline that will  │
│   serve as baseline for the next experiments that we are going to run  │
│   together.                                                            │
╰────────────────────────────────────────────────────────────────────────╯

you can expect your agent to start experimenting with you. The skills work well with models such as Claude Opus and Sonnet and produce great results with smaller models such as Qwen 3.6 30B or DeepSeek v4 Flash.

As for agent harnesses, we tested them with Claude Code, OpenCode, Cursor, and GitHub Copilot and found no significant difference in terms of skill invocation.

Install

You can install the skills using the skore CLI that you can install from PyPI or from conda-forge and run the following command.

First install skore-cli:

# with pip
pip install skore-cli
with uv
uv tool install skore-cli
with pixi
pixi global install skore-cli

Then run the following command:

skore skills install

You can use uvx or pixi exec to install the skore CLI and directly run the command in an isolated environment:

uvx --from skore-cli skore skills install

or

pixi exec --spec skore-cli skore skills install

If you prefer npx, then you can use:

npx skills add probabl-ai/skills

Alternative — Claude Code plugin marketplace

If you only use Claude Code and prefer the native plugin flow, this repo is also a Claude Code plugin marketplace:

/plugin marketplace add probabl-ai/skills

/plugin install probabl-skills@probabl-skills

/plugin update pulls new releases.

Skills in detail

ML pipeline lifecycle

| Skill | Description | | --- | --- | | explore-ml-data | Explore the dataset before designing any model. | | build-ml-pipeline | Build a machine learning pipeline from the data source to the learner, including multi-tables engineering. | | evaluate-ml-pipeline | Evaluate a complex machine learning pipeline and get structured reports including metrics, plots, and diagnostics. | | test-ml-pipeline | Make sure that your machine learning pipeline is production-ready statistically and functionally. | | smoke-test-ml-pipeline | Stress test your machine learning pipeline on future data to make sure it works. | | audit-ml-pipeline | Once testing and the experiment are done, audit the model by loading a skore report and investigate. |

Iteration loop

| Skill | Description | | --- | --- | | iterate-ml-experiment | Design, keep track of experiments, and iterate on them. | | iterate-from-skore | Use skore to run diagnostics and checks that can be reported and addressed in the next experiment. | | iterate-from-user | As a user, be in the loop and propose new experiments — free-text, a scientific article URL, or a resource link (GitHub issue / spec / reference repo). |

Workspace and tooling

| Skill | Description | | --- | --- | | organize-ml-workspace | An organized workspace to keep track of your experiments. | | python-code-style | Enforce best practices out-of-the-box from the Python ecosystem in your code. | | python-env-manager | Bootstrapping the experiment setup based on your favorite Python environment manager. | | data-science-python-stack | Opinionated one-library-per-job Python stack, organized into mandatory / user-choice / optional / transitive tiers. |

API references

| Skill | Description | | --- | --- | | python-api | Discover the public API of any installed Python package to help agents navigate without polluting your workspace. |